Preliminary Results
This document presents an initial analysis of the household survey responses from invited residents living within three areas of Charlottesville and Albemarle County. The survey itself was constructed using Qualtrics and ESRI Survey 123—participants were recruited using mailed postcards and via email. The household survey response rate was lower than expected, despite compensation in the form of a $35 gift card as a financial incentive and we received 62 responses from 462 invitations for a response rate of 13.4% percent. Note: the effective response rate still needs to be calculated by taking into account the number of postcards that were “returned to sender” or marked as undeliverable by the US Postal Service.
However, only 54 of those 62 respondents completed the survey questionnaire to a degree that is useful for analysis.
Although we did not collect any demographic information, we do know where the respondents live who provided a survey access code. The interactive map below shows the location of all respondents and of those who fully completed the survey questionnaire.
knitr::kable(
data_1 %>%
group_by(Neighborhood) %>%
count(), col.names = c("Neighborhood", "Responses")
) | Neighborhood | Responses |
|---|---|
| 10th and Page Neighborhood | 14 |
| Fashion Square Mall and Branchlands Area | 14 |
| Four Seasons Apartments and Townwood Mobile Home Park | 24 |
| Michie Drive Subsidized Housing | 2 |
tmap_mode("view")ℹ tmap mode set to "view".# tmap_options(check.and.fix = T)
tm_shape(sites_sf, name = "Study Areas") +
tm_polygons(fill = "yellow", col = "yellow2", lwd = 2,
fill_alpha = 0.25,
popup.vars = c("Name: " = "Descriptio")) +
tm_title(text = "Survey Responses by Study Area") +
tm_shape(already_responded_deets_sf, name = "Responses") +
tm_dots(fill = "Completed_Survey", fill_alpha = 0.75, size = 0.75, shape = 21,
popup.vars = c("Name: " = "RECIPIENT", "Address: " = "FullAddress",
"Phone? " = "HasPhone", "Email? " = "HasEmail"),
fill.scale = tm_scale_categorical(n = 2, values = c("green4", "red")),
fill.legend = tm_legend(title = "Completed Survey?")) +
tm_layout(frame = F) Of the 54 usable responses, 27 participants indicated that they would like an invitation to the follow-up focus group meeting:
The first few questions ask about respondents’ familiarity with NbS concepts and terminology:
Are you familiar with any of these terms: nature-based solution (NbS), green infrastructure, best management practice (BMPs), or Low Impact Development (LID)?
Are you a homeowner, water resources professional, or industrial/commercial landowner?
Would it be possible for you to convert impervious/paved surfaces to grass, gardens, or trees on your property?
What are the major barriers that would prevent you from implementing NbS on your property, if any?
# Are you familiar with any of these terms...
familiar_nbs <- recoded_data %>%
dplyr::select(Neighborhood, Familiar_Terms) %>%
group_by(Neighborhood) %>%
tabyl(Neighborhood, Familiar_Terms) %>%
`colnames<-`(c("Neighborhood", "No", "Yes")) %>%
ggplot() +
geom_bar(aes(y = Yes, x = Neighborhood), stat = "identity", fill = "green4", alpha = 0.75) +
labs(title = "Familiar With NbS Terminology", y = "", x = "") +
scale_x_discrete(labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
axis.text.x = element_text(size = 12),
text = element_text(size = 18)) +
geom_text(aes(label = Yes, x = Neighborhood, y = Yes), vjust = 1.8, color = "white", size = 7)
not_familiar_nbs <- recoded_data %>%
dplyr::select(Neighborhood, Familiar_Terms) %>%
group_by(Neighborhood) %>%
tabyl(Neighborhood, Familiar_Terms) %>%
`colnames<-`(c("Neighborhood", "No", "Yes")) %>%
ggplot() +
geom_bar(aes(y = No, x = Neighborhood), stat = "identity", fill = "#E57200", alpha = 0.75) +
labs(title = "Not Familiar With NbS Terminology", y = "", x = "") +
scale_x_discrete(labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
axis.text.x = element_text(size = 12),
text = element_text(size = 18)) +
geom_text(aes(label = No, x = Neighborhood, y = No), vjust = 1.8, color = "white", size = 7)
ggarrange(familiar_nbs, not_familiar_nbs)
# Are you a homeowner, water resources professional, or industrial/commercial landowner?
kable(
recoded_data %>%
group_by(Role) %>%
count() %>%
as.data.frame() %>%
`rownames<-`(c("None of These", "Homeowner", "Water Resources Professional")),
col.names = c("Role", "Responses")
)| Role | Responses | |
|---|---|---|
| None of These | 0 | 25 |
| Homeowner | 1 | 28 |
| Water Resources Professional | 2 | 1 |
recoded_data %>%
dplyr::select(Neighborhood, Role) %>%
group_by(Neighborhood) %>%
tabyl(Neighborhood, Role) %>%
`colnames<-`(c("Neighborhood", "None of These", "Homeowner", "Water Resources Professional")) %>%
ggplot() +
geom_bar(aes(y = Homeowner, x = Neighborhood), stat = "identity", fill = "dodgerblue", alpha = 0.75) +
labs(title = "I Am A Homeowner", y = "", x = "") +
scale_x_discrete(labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
axis.text.x = element_text(size = 12),
text = element_text(size = 18)) +
geom_text(aes(label = Homeowner, x = Neighborhood, y = Homeowner), vjust = 1.8, color = "white", size = 7)
# Would it be possible for you to convert impervious/paved surfaces to grass, gardens, or trees on your property?
# table(recoded_data$Convert_Impervious)
ggplot(data = recoded_data) +
geom_bar(aes(x = as_factor(Convert_Impervious)), stat = "count", fill = "yellow3", alpha = 0.75) +
labs(title = "Is It Possible For You to Convert Impervious Surfaces to Grass, \n Gardens, or Trees on Your Property?", y = "", x = "") +
scale_x_discrete(labels = c("Other", "I am not sure", "Yes, but it would be difficult", "Yes, it would be possible")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
axis.text.x = element_text(size = 12),
text = element_text(size = 18)) +
geom_text(aes(label = after_stat(count), x = as_factor(Convert_Impervious)), vjust = 1.8, color = "white", size = 7, stat = "count")
# What are the major barriers that would prevent you from implementing NbS on your property, if any?
barriers_in_order <- c("Cost_", "Uncertain_Benefits_", "Maintenance_and_Upkeep_", "Unanticipated_Impacts_", "Not_Enough_Space_", "other")
NBS_Barriers_Top_3 %>%
tabyl() %>%
`colnames<-`(c("Barrier", "Number", "Percent")) %>%
ggplot() +
geom_bar(aes(y = Number, x = factor(Barrier, barriers_in_order)), stat="identity", fill = "#E57200", alpha = 0.85) +
scale_x_discrete(labels = c("Cost", "Uncertain Benefits", "Maintenance and Upkeep", "Unanticipated Impacts", "Not Enough Space", "Other"),
name = "Barrier") +
labs(title = "Frequency of Barriers in Top 3 Selections") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
axis.text.x = element_text(size = 12),
text = element_text(size = 18)) +
geom_text(aes(label = Number, x = Barrier, y = Number), vjust = 1.8, color = "white", size = 7)
to_drop <- c("N/a", "N/A", "n/a")
barriers_other <- data_1 %>%
filter(is.na(`Other - What are the major barriers that would prevent you from implementing NbS on your property, if any?`) == FALSE) %>%
dplyr::select(`Other - What are the major barriers that would prevent you from implementing NbS on your property, if any?`, Neighborhood) %>%
filter(! `Other - What are the major barriers that would prevent you from implementing NbS on your property, if any?` %in% to_drop)
kbl(
barriers_other %>%
arrange(Neighborhood)
) %>%
kable_paper(bootstrap_options = "striped", full_width = F)| Other - What are the major barriers that would prevent you from implementing NbS on your property, if any? | Neighborhood |
|---|---|
| I don't know what this is | 10th and Page Neighborhood |
| Not owner | 10th and Page Neighborhood |
| not a home owner | 10th and Page Neighborhood |
| An HOA that actively thwarts all positive environmental efforts. For example, no one is permitted any solar panels, and pollinator gardens are cited for being too messy. | Fashion Square Mall and Branchlands Area |
| HOA approval (lack of) | Fashion Square Mall and Branchlands Area |
| I own a townhouse but not the land it sits on. My native plants were cut back last fall without my permission. The two young oak trees were left alone. | Fashion Square Mall and Branchlands Area |
| I live in a property owner's association so the Board and possible a majority of owners would have to approve | Fashion Square Mall and Branchlands Area |
| Do not own property | Fashion Square Mall and Branchlands Area |
| I live in a condo | Fashion Square Mall and Branchlands Area |
| My home is a condo townhouse so the HOA owns all the exterior pavement trees etc so it would take HOA to do Nbs for the whole neighborhood | Fashion Square Mall and Branchlands Area |
| The HOA has denied prior plan that my sister (the homeowner) and I submitted that included NbS | Four Seasons Apartments and Townwood Mobile Home Park |
| HOA restrictions | Four Seasons Apartments and Townwood Mobile Home Park |
| Potential HOA restrictions | Four Seasons Apartments and Townwood Mobile Home Park |
| Lease agreement | Four Seasons Apartments and Townwood Mobile Home Park |
| Home Association Rules and Guidelines | Four Seasons Apartments and Townwood Mobile Home Park |
| I do not own any property | Michie Drive Subsidized Housing |
The overwhelming majority of respondents identified as homeowners (28 of 54) and the percentage of respondents who were familiar with NbS terminology lived in the Fashion Square Mall and Branchlands Area (i.e., 12 of 14). Familiarity with NbS terminology was lowest among 10th and Page respondents (i.e., 9 of 14). The implication is that 26 of the respondents are renters.
A majority of respondents were unsure or doubtful (33 of 54) of their ability to convert existing impervious surfaces on their property with cost and other factors (see the table) like HOA restrictions most frequently cited.
Two of the survey questions ask respondents to rank the benefits associated with a list of NbS interventions that this project considers and to identify related environmental impacts that are of most concern.
Please rank each of the benefits associated with NbS listed below in terms of their importance to you on a scale from 1 to 8 with 1 indicating the most important:
Which of the following would you say is of greatest concern to your community?
# Please rank each of the benefits associated with NbS listed below in terms of their importance
NBS_Import_Top_3 %>%
tabyl() %>%
`colnames<-`(c("Benefit", "Number", "Percent")) %>%
ggplot() +
geom_bar(aes(y = Number, x = Benefit), stat="identity", fill = "dodgerblue", alpha = 0.75) +
scale_x_discrete(labels = c("Air Quality \n Improvements", "Enhanced Landscape \n Aesthetics",
"Environmental Education \n Opportunities", "Increased Habitat \n for Native Plants \n and Animals",
"Recreational \n Opportunities", "Stormwater Runoff \n and Flooding Mitigation",
"Urban Heat \n Island Attenuation", "Water Supply" ), name = "Benefit") +
labs(title = "Frequency of Benefits in Top 3 Selections") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
axis.text.x = element_text(size = 12),
text = element_text(size = 18)) +
geom_text(aes(label = Number, x = Benefit, y = Number), vjust = 1.8, color = "white", size = 7)
# Which of the following would you say is of greatest concern to your community
NBS_Greatest_Concern %>%
tabyl() %>%
`colnames<-`(c("Concern", "Number", "Percent")) %>%
ggplot() +
geom_bar(aes(y = Number, x = Concern), stat="identity", fill = "green4", alpha = 0.75) +
scale_x_discrete(labels = c("Air Pollution", "Flooding/Stormwater \n Management",
"Urban Heat", "Water Quality" ), name = "Concern") +
labs(title = "Frequency of Greatest Concern") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
axis.text.x = element_text(size = 12),
text = element_text(size = 18)) +
geom_text(aes(label = Number, x = Concern, y = Number), vjust = 1.8, color = "white", size = 7)
Stormwater runoff and flood mitigation benefits were most frequently cited NbS benefits in terms of their importance. Similarly, most respondents identified flood and stormwater management followed y water quality as the greatest concern where they live.
There are several sets of questions designed to capture respondent perceptions of the five NbS interventions this project has considered—rain gardens, green roofs, green spaces (e.g., parks and fields), stormwater retention ponds, and tree planting. For each of these interventions there is a Likert scale question that asks about that intervention’s capacity to enhance local water supplies, recreational opportunities, habitat for native plants/animals, to beautify the landscape, and to help prevent flooding.
For each of these responses a higher represents a more positive perception of that NbS and its capacity to deliver the benefit in question. These questions are all coded 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree and an example suite of these questions is given below:
In addition to selected descriptive statistics, we use non-parametric Kruskal-Wallis tests to explore potential differences in outcomes of interest. Note that the null hypothesis for the Kruskal-Wallis test is that the median values of the variable of interest are the same across the groups being compared—this test is useful when the data are not normally distributed or when they are ordinal (e.g., Likert scales).
kbl(
recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Rain_Garden_Water_Supplies, Rain_Garden_Recreation, Rain_Garden_Habitat,
Rain_Garden_Beautify, Rain_Garden_Prevent_Flooding, Neighborhood) %>%
`colnames<-`(c("Rain_Garden_Water_Supplies", "Rain_Garden_Recreation", "Rain_Garden_Habitat",
"Rain_Garden_Beautify", "Rain_Garden_Prevent_Flooding", "Neighborhood")) %>%
rstatix::get_summary_stats(show = c("min", "max", "mean", "sd")),
col.names = c("Neighborhood", "Question", "#", "Min", "Max", "Mean", "Std Dev"),
) %>%
kable_paper(bootstrap_options = "striped", full_width = F) | Neighborhood | Question | # | Min | Max | Mean | Std Dev |
|---|---|---|---|---|---|---|
| 10th and Page Neighborhood | Rain_Garden_Water_Supplies | 14 | 1 | 5 | 3.571 | 1.016 |
| 10th and Page Neighborhood | Rain_Garden_Recreation | 14 | 1 | 4 | 3.286 | 1.267 |
| 10th and Page Neighborhood | Rain_Garden_Habitat | 14 | 1 | 5 | 3.500 | 1.286 |
| 10th and Page Neighborhood | Rain_Garden_Beautify | 14 | 1 | 5 | 3.929 | 1.072 |
| 10th and Page Neighborhood | Rain_Garden_Prevent_Flooding | 14 | 2 | 5 | 3.571 | 1.016 |
| Fashion Square Mall and Branchlands Area | Rain_Garden_Water_Supplies | 14 | 1 | 5 | 4.071 | 1.072 |
| Fashion Square Mall and Branchlands Area | Rain_Garden_Recreation | 14 | 1 | 5 | 3.857 | 1.292 |
| Fashion Square Mall and Branchlands Area | Rain_Garden_Habitat | 14 | 1 | 5 | 4.429 | 1.158 |
| Fashion Square Mall and Branchlands Area | Rain_Garden_Beautify | 14 | 1 | 5 | 4.000 | 1.240 |
| Fashion Square Mall and Branchlands Area | Rain_Garden_Prevent_Flooding | 14 | 1 | 5 | 4.214 | 1.122 |
| Four Seasons Apartments and Townwood Mobile Home Park | Rain_Garden_Water_Supplies | 23 | 2 | 5 | 3.957 | 0.878 |
| Four Seasons Apartments and Townwood Mobile Home Park | Rain_Garden_Recreation | 24 | 2 | 5 | 3.917 | 0.830 |
| Four Seasons Apartments and Townwood Mobile Home Park | Rain_Garden_Habitat | 24 | 2 | 5 | 4.250 | 0.847 |
| Four Seasons Apartments and Townwood Mobile Home Park | Rain_Garden_Beautify | 24 | 3 | 5 | 4.417 | 0.717 |
| Four Seasons Apartments and Townwood Mobile Home Park | Rain_Garden_Prevent_Flooding | 24 | 3 | 5 | 4.000 | 0.780 |
| Michie Drive Subsidized Housing | Rain_Garden_Water_Supplies | 2 | 3 | 5 | 4.000 | 1.414 |
| Michie Drive Subsidized Housing | Rain_Garden_Recreation | 2 | 3 | 4 | 3.500 | 0.707 |
| Michie Drive Subsidized Housing | Rain_Garden_Habitat | 2 | 4 | 5 | 4.500 | 0.707 |
| Michie Drive Subsidized Housing | Rain_Garden_Beautify | 2 | 4 | 5 | 4.500 | 0.707 |
| Michie Drive Subsidized Housing | Rain_Garden_Prevent_Flooding | 2 | 3 | 5 | 4.000 | 1.414 |
kruskal_test(data = recoded_data, formula = Rain_Garden_Water_Supplies ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Rain_Garden_Water_Supplies 54 2.71 3 0.438 Kruskal-Walliswilcox_test(formula = Rain_Garden_Water_Supplies ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Rain_Garden_Wate… 10th … Fashi… 14 14 64 0.1 0.599 ns
2 Rain_Garden_Wate… 10th … Four … 14 23 128 0.282 1 ns
3 Rain_Garden_Wate… 10th … Michi… 14 2 11.5 0.737 1 ns
4 Rain_Garden_Wate… Fashi… Four … 14 23 182 0.493 1 ns
5 Rain_Garden_Wate… Fashi… Michi… 14 2 15 0.932 1 ns
6 Rain_Garden_Wate… Four … Michi… 23 2 22.5 1 1 ns kruskal_test(data = recoded_data, formula = Rain_Garden_Recreation ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Rain_Garden_Recreation 54 3.31 3 0.347 Kruskal-Walliswilcox_test(formula = Rain_Garden_Recreation ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Rain_Garden_Recr… 10th … Fashi… 14 14 64.5 0.091 0.547 ns
2 Rain_Garden_Recr… 10th … Four … 14 24 129 0.199 0.995 ns
3 Rain_Garden_Recr… 10th … Michi… 14 2 15.5 0.846 1 ns
4 Rain_Garden_Recr… Fashi… Four … 14 24 181 0.687 1 ns
5 Rain_Garden_Recr… Fashi… Michi… 14 2 19 0.448 1 ns
6 Rain_Garden_Recr… Four … Michi… 24 2 31.5 0.47 1 ns kruskal_test(data = recoded_data, formula = Rain_Garden_Habitat ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Rain_Garden_Habitat 54 7.80 3 0.0504 Kruskal-Walliswilcox_test(formula = Rain_Garden_Habitat ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Rain_Garden_Habi… 10th … Fashi… 14 14 45.5 0.011 0.064 ns
2 Rain_Garden_Habi… 10th … Four … 14 24 108 0.054 0.268 ns
3 Rain_Garden_Habi… 10th … Michi… 14 2 7 0.253 0.9 ns
4 Rain_Garden_Habi… Fashi… Four … 14 24 204. 0.225 0.9 ns
5 Rain_Garden_Habi… Fashi… Michi… 14 2 16 0.771 1 ns
6 Rain_Garden_Habi… Four … Michi… 24 2 21 0.793 1 ns kruskal_test(data = recoded_data, formula = Rain_Garden_Beautify ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Rain_Garden_Beautify 54 2.52 3 0.472 Kruskal-Walliswilcox_test(formula = Rain_Garden_Beautify ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Rain_Garden_Beau… 10th … Fashi… 14 14 88 0.641 1 ns
2 Rain_Garden_Beau… 10th … Four … 14 24 122 0.135 0.81 ns
3 Rain_Garden_Beau… 10th … Michi… 14 2 9.5 0.49 1 ns
4 Rain_Garden_Beau… Fashi… Four … 14 24 142. 0.389 1 ns
5 Rain_Garden_Beau… Fashi… Michi… 14 2 11.5 0.733 1 ns
6 Rain_Garden_Beau… Four … Michi… 24 2 23.5 1 1 ns kruskal_test(data = recoded_data, formula = Rain_Garden_Prevent_Flooding ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Rain_Garden_Prevent_Flooding 54 4.25 3 0.235 Kruskal-Walliswilcox_test(formula = Rain_Garden_Prevent_Flooding ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Rain_Garden_Prev… 10th … Fashi… 14 14 59 0.064 0.384 ns
2 Rain_Garden_Prev… 10th … Four … 14 24 127 0.197 0.985 ns
3 Rain_Garden_Prev… 10th … Michi… 14 2 11 0.679 1 ns
4 Rain_Garden_Prev… Fashi… Four … 14 24 207 0.214 0.985 ns
5 Rain_Garden_Prev… Fashi… Michi… 14 2 16 0.796 1 ns
6 Rain_Garden_Prev… Four … Michi… 24 2 24 1 1 ns recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Rain_Garden_Habitat, Neighborhood) %>%
rstatix::get_summary_stats(type = "common") %>%
ggplot() +
geom_bar(aes(x = Neighborhood, y = mean, fill = Neighborhood), stat = "identity", alpha = 0.6) +
scale_fill_manual(name = "", values = c("#440154FF", "dodgerblue", "#E57200", "#B8DE29FF"),
labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
labs(x = " ", y = "Mean", title = "Perceptions of Rain Garden Habitat Benefits", subtitle = "",
caption = "Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree") +
theme_minimal() +
theme(legend.position="bottom",
plot.subtitle = element_text(hjust = 0.5),
plot.title = element_text(hjust = 0.5, size = 16),
axis.text.y = element_text(size = 12),
plot.caption = element_text(size = 9),
text = element_text(size = 14)) +
geom_text(aes(label = round(mean, 2), x = Neighborhood, y = mean), vjust = 1.8, color = "white", size = 7)
There are no statistically significant differences in any of the questions related to perceived benefits of rain gardens across neighborhoods based on the Kruskal-Wallis and Wilcox tests.
kbl(
recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Green_Roof_Water_Supplies, Green_Roof_Recreation, Green_Roof_Habitat,
Green_Roof_Beautify, Green_Roof_Prevent_Flooding, Neighborhood) %>%
`colnames<-`(c("Green_Roof_Water_Supplies", "Green_Roof_Recreation", "Green_Roof_Habitat",
"Green_Roof_Beautify", "Green_Roof_Prevent_Flooding", "Neighborhood")) %>%
rstatix::get_summary_stats(show = c("min", "max", "mean", "sd")),
col.names = c("Neighborhood", "Question", "#", "Min", "Max", "Mean", "Std Dev"),
) %>%
kable_paper(bootstrap_options = "striped", full_width = F) | Neighborhood | Question | # | Min | Max | Mean | Std Dev |
|---|---|---|---|---|---|---|
| 10th and Page Neighborhood | Green_Roof_Water_Supplies | 14 | 3 | 5 | 3.643 | 0.745 |
| 10th and Page Neighborhood | Green_Roof_Recreation | 14 | 3 | 4 | 3.500 | 0.519 |
| 10th and Page Neighborhood | Green_Roof_Habitat | 14 | 1 | 5 | 3.500 | 0.941 |
| 10th and Page Neighborhood | Green_Roof_Beautify | 14 | 1 | 4 | 3.071 | 1.141 |
| 10th and Page Neighborhood | Green_Roof_Prevent_Flooding | 14 | 2 | 5 | 3.500 | 0.760 |
| Fashion Square Mall and Branchlands Area | Green_Roof_Water_Supplies | 14 | 1 | 5 | 3.643 | 1.277 |
| Fashion Square Mall and Branchlands Area | Green_Roof_Recreation | 14 | 1 | 5 | 3.429 | 1.158 |
| Fashion Square Mall and Branchlands Area | Green_Roof_Habitat | 14 | 2 | 5 | 4.357 | 0.929 |
| Fashion Square Mall and Branchlands Area | Green_Roof_Beautify | 14 | 2 | 5 | 3.929 | 0.997 |
| Fashion Square Mall and Branchlands Area | Green_Roof_Prevent_Flooding | 14 | 3 | 5 | 4.214 | 0.802 |
| Four Seasons Apartments and Townwood Mobile Home Park | Green_Roof_Water_Supplies | 24 | 2 | 5 | 3.333 | 0.816 |
| Four Seasons Apartments and Townwood Mobile Home Park | Green_Roof_Recreation | 24 | 2 | 5 | 3.458 | 0.779 |
| Four Seasons Apartments and Townwood Mobile Home Park | Green_Roof_Habitat | 24 | 3 | 5 | 4.125 | 0.741 |
| Four Seasons Apartments and Townwood Mobile Home Park | Green_Roof_Beautify | 24 | 3 | 5 | 4.042 | 0.550 |
| Four Seasons Apartments and Townwood Mobile Home Park | Green_Roof_Prevent_Flooding | 24 | 2 | 5 | 3.792 | 0.932 |
| Michie Drive Subsidized Housing | Green_Roof_Water_Supplies | 2 | 3 | 3 | 3.000 | 0.000 |
| Michie Drive Subsidized Housing | Green_Roof_Recreation | 2 | 3 | 4 | 3.500 | 0.707 |
| Michie Drive Subsidized Housing | Green_Roof_Habitat | 2 | 4 | 5 | 4.500 | 0.707 |
| Michie Drive Subsidized Housing | Green_Roof_Beautify | 2 | 4 | 5 | 4.500 | 0.707 |
| Michie Drive Subsidized Housing | Green_Roof_Prevent_Flooding | 2 | 3 | 3 | 3.000 | 0.000 |
kruskal_test(data = recoded_data, formula = Green_Roof_Water_Supplies ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Green_Roof_Water_Supplies 54 3.12 3 0.373 Kruskal-Walliswilcox_test(formula = Green_Roof_Water_Supplies ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Green_Roof_Water… 10th … Fashi… 14 14 89.5 0.7 1 ns
2 Green_Roof_Water… 10th … Four … 14 24 206. 0.196 1 ns
3 Green_Roof_Water… 10th … Michi… 14 2 21 0.246 1 ns
4 Green_Roof_Water… Fashi… Four … 14 24 206 0.229 1 ns
5 Green_Roof_Water… Fashi… Michi… 14 2 20 0.368 1 ns
6 Green_Roof_Water… Four … Michi… 24 2 29 0.609 1 ns kruskal_test(data = recoded_data, formula = Green_Roof_Recreation ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Green_Roof_Recreation 54 0.0967 3 0.992 Kruskal-Walliswilcox_test(formula = Green_Roof_Recreation ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Green_Roof_Recre… 10th … Fashi… 14 14 94.5 0.881 1 ns
2 Green_Roof_Recre… 10th … Four … 14 24 175 0.828 1 ns
3 Green_Roof_Recre… 10th … Michi… 14 2 14 1 1 ns
4 Green_Roof_Recre… Fashi… Four … 14 24 176. 0.822 1 ns
5 Green_Roof_Recre… Fashi… Michi… 14 2 14.5 1 1 ns
6 Green_Roof_Recre… Four … Michi… 24 2 23 0.958 1 ns kruskal_test(data = recoded_data, formula = Green_Roof_Habitat ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Green_Roof_Habitat 54 8.24 3 0.0412 Kruskal-Walliswilcox_test(formula = Green_Roof_Habitat ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Green_Roof_Habit… 10th … Fashi… 14 14 45.5 0.012 0.07 ns
2 Green_Roof_Habit… 10th … Four … 14 24 106 0.045 0.226 ns
3 Green_Roof_Habit… 10th … Michi… 14 2 5 0.142 0.568 ns
4 Green_Roof_Habit… Fashi… Four … 14 24 204. 0.24 0.72 ns
5 Green_Roof_Habit… Fashi… Michi… 14 2 14 1 1 ns
6 Green_Roof_Habit… Four … Michi… 24 2 17.5 0.532 1 ns kruskal_test(data = recoded_data, formula = Green_Roof_Beautify ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Green_Roof_Beautify 54 9.10 3 0.028 Kruskal-Walliswilcox_test(formula = Green_Roof_Beautify ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Green_Roof_Beaut… 10th … Fashi… 14 14 59 0.065 0.324 ns
2 Green_Roof_Beaut… 10th … Four … 14 24 85 0.004 0.023 *
3 Green_Roof_Beaut… 10th … Michi… 14 2 3.5 0.088 0.353 ns
4 Green_Roof_Beaut… Fashi… Four … 14 24 162 0.853 1 ns
5 Green_Roof_Beaut… Fashi… Michi… 14 2 9.5 0.504 1 ns
6 Green_Roof_Beaut… Four … Michi… 24 2 14.5 0.287 0.861 ns kruskal_test(data = recoded_data, formula = Green_Roof_Prevent_Flooding ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Green_Roof_Prevent_Flooding 54 6.73 3 0.081 Kruskal-Walliswilcox_test(formula = Green_Roof_Prevent_Flooding ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Green_Roof_Preve… 10th … Fashi… 14 14 53 0.03 0.182 ns
2 Green_Roof_Preve… 10th … Four … 14 24 136 0.313 0.744 ns
3 Green_Roof_Preve… 10th … Michi… 14 2 20 0.336 0.744 ns
4 Green_Roof_Preve… Fashi… Four … 14 24 210 0.186 0.744 ns
5 Green_Roof_Preve… Fashi… Michi… 14 2 25 0.077 0.386 ns
6 Green_Roof_Preve… Four … Michi… 24 2 37 0.206 0.744 ns recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Green_Roof_Habitat, Neighborhood) %>%
rstatix::get_summary_stats(type = "common") %>%
ggplot() +
geom_bar(aes(x = Neighborhood, y = mean, fill = Neighborhood), stat = "identity", alpha = 0.6) +
scale_fill_manual(name = "", values = c("#440154FF", "dodgerblue", "#E57200", "#B8DE29FF"),
labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
labs(x = " ", y = "Mean", title = "Perceptions of Green Roof Habitat Benefits", subtitle = "",
caption = "Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree") +
theme_minimal() +
theme(legend.position="bottom",
plot.subtitle = element_text(hjust = 0.5),
plot.title = element_text(hjust = 0.5, size = 16),
axis.text.y = element_text(size = 12),
plot.caption = element_text(size = 9),
text = element_text(size = 14)) +
geom_text(aes(label = round(mean, 2), x = Neighborhood, y = mean), vjust = 1.8, color = "white", size = 7)
recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Green_Roof_Beautify, Neighborhood) %>%
rstatix::get_summary_stats(type = "common") %>%
ggplot() +
geom_bar(aes(x = Neighborhood, y = mean, fill = Neighborhood), stat = "identity", alpha = 0.6) +
scale_fill_manual(name = "", values = c("#440154FF", "dodgerblue", "#E57200", "#B8DE29FF"),
labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
labs(x = " ", y = "Mean", title = "Perceptions of Green Roof Beautification Benefits", subtitle = "",
caption = "Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree") +
theme_minimal() +
theme(legend.position="bottom",
plot.subtitle = element_text(hjust = 0.5),
plot.title = element_text(hjust = 0.5, size = 16),
axis.text.y = element_text(size = 12),
plot.caption = element_text(size = 9),
text = element_text(size = 14)) +
geom_text(aes(label = round(mean, 2), x = Neighborhood, y = mean), vjust = 1.8, color = "white", size = 7)
There are statistically significant differences (0.05 alpha level) for two questions related to perceived benefits of green roofs across neighborhoods.
For the Green roofs can increase habitat for native plants and animals question (kw = 8.24, p = 0.041) respondents from the Michie Drive area agreed most strongly while respondents from 10th and Page were most neutral. The Wilcox test did not corroborate this though.
For the Green roofs can enhance the beautification of the landscape question (kw = 9.10, p = 0.028) respondents from the Michie Drive area agreed most strongly while respondents from 10th and Page were most neutral. The Wilcox test confirms a significant difference between 10th and Page responses and those from the Four Seasons Apartments and Townwood Mobile Home Park.
kbl(
recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Parks_Fields_Water_Supplies, Parks_Fields_Recreation, Parks_Fields_Habitat,
Parks_Fields_Beautify, Parks_Fields_Prevent_Flooding, Neighborhood) %>%
`colnames<-`(c("Parks_Fields_Water_Supplies", "Parks_Fields_Recreation", "Parks_Fields_Habitat",
"Parks_Fields_Beautify", "Parks_Fields_Prevent_Flooding", "Neighborhood")) %>%
rstatix::get_summary_stats(show = c("min", "max", "mean", "sd")),
col.names = c("Neighborhood", "Question", "#", "Min", "Max", "Mean", "Std Dev"),
) %>%
kable_paper(bootstrap_options = "striped", full_width = F) | Neighborhood | Question | # | Min | Max | Mean | Std Dev |
|---|---|---|---|---|---|---|
| 10th and Page Neighborhood | Parks_Fields_Water_Supplies | 14 | 2 | 5 | 3.571 | 0.852 |
| 10th and Page Neighborhood | Parks_Fields_Recreation | 13 | 2 | 5 | 4.000 | 0.913 |
| 10th and Page Neighborhood | Parks_Fields_Habitat | 13 | 3 | 5 | 4.385 | 0.768 |
| 10th and Page Neighborhood | Parks_Fields_Beautify | 14 | 1 | 5 | 3.857 | 1.351 |
| 10th and Page Neighborhood | Parks_Fields_Prevent_Flooding | 14 | 1 | 5 | 3.357 | 1.082 |
| Fashion Square Mall and Branchlands Area | Parks_Fields_Water_Supplies | 14 | 3 | 5 | 4.500 | 0.650 |
| Fashion Square Mall and Branchlands Area | Parks_Fields_Recreation | 14 | 3 | 5 | 4.500 | 0.760 |
| Fashion Square Mall and Branchlands Area | Parks_Fields_Habitat | 14 | 3 | 5 | 4.429 | 0.756 |
| Fashion Square Mall and Branchlands Area | Parks_Fields_Beautify | 14 | 4 | 5 | 4.643 | 0.497 |
| Fashion Square Mall and Branchlands Area | Parks_Fields_Prevent_Flooding | 14 | 3 | 5 | 4.429 | 0.756 |
| Four Seasons Apartments and Townwood Mobile Home Park | Parks_Fields_Water_Supplies | 24 | 2 | 5 | 3.875 | 0.797 |
| Four Seasons Apartments and Townwood Mobile Home Park | Parks_Fields_Recreation | 24 | 2 | 5 | 4.083 | 0.974 |
| Four Seasons Apartments and Townwood Mobile Home Park | Parks_Fields_Habitat | 24 | 2 | 5 | 4.292 | 0.859 |
| Four Seasons Apartments and Townwood Mobile Home Park | Parks_Fields_Beautify | 24 | 3 | 5 | 4.292 | 0.806 |
| Four Seasons Apartments and Townwood Mobile Home Park | Parks_Fields_Prevent_Flooding | 24 | 2 | 5 | 3.833 | 0.868 |
| Michie Drive Subsidized Housing | Parks_Fields_Water_Supplies | 2 | 3 | 3 | 3.000 | 0.000 |
| Michie Drive Subsidized Housing | Parks_Fields_Recreation | 2 | 4 | 4 | 4.000 | 0.000 |
| Michie Drive Subsidized Housing | Parks_Fields_Habitat | 2 | 4 | 5 | 4.500 | 0.707 |
| Michie Drive Subsidized Housing | Parks_Fields_Beautify | 2 | 4 | 5 | 4.500 | 0.707 |
| Michie Drive Subsidized Housing | Parks_Fields_Prevent_Flooding | 2 | 4 | 4 | 4.000 | 0.000 |
kruskal_test(data = recoded_data, formula = Parks_Fields_Water_Supplies ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Parks_Fields_Water_Supplies 54 12.0 3 0.00737 Kruskal-Walliswilcox_test(formula = Parks_Fields_Water_Supplies ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Parks_Fields_Wat… 10th … Fashi… 14 14 40.5 0.006 0.034 *
2 Parks_Fields_Wat… 10th … Four … 14 24 132. 0.257 0.514 ns
3 Parks_Fields_Wat… 10th … Michi… 14 2 20 0.342 0.514 ns
4 Parks_Fields_Wat… Fashi… Four … 14 24 241 0.018 0.092 ns
5 Parks_Fields_Wat… Fashi… Michi… 14 2 27 0.03 0.122 ns
6 Parks_Fields_Wat… Four … Michi… 24 2 40 0.109 0.327 ns kruskal_test(data = recoded_data, formula = Parks_Fields_Recreation ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Parks_Fields_Recreation 54 3.17 3 0.366 Kruskal-Walliswilcox_test(formula = Parks_Fields_Recreation ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Parks_Fields_Rec… 10th … Fashi… 13 14 61 0.12 0.72 ns
2 Parks_Fields_Rec… 10th … Four … 13 24 145 0.723 1 ns
3 Parks_Fields_Rec… 10th … Michi… 13 2 14 0.926 1 ns
4 Parks_Fields_Rec… Fashi… Four … 14 24 209 0.183 0.915 ns
5 Parks_Fields_Rec… Fashi… Michi… 14 2 21 0.246 0.984 ns
6 Parks_Fields_Rec… Four … Michi… 24 2 28 0.72 1 ns kruskal_test(data = recoded_data, formula = Parks_Fields_Habitat ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Parks_Fields_Habitat 54 0.218 3 0.975 Kruskal-Walliswilcox_test(formula = Parks_Fields_Habitat ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Parks_Fields_Hab… 10th … Fashi… 13 14 88 0.892 1 ns
2 Parks_Fields_Hab… 10th … Four … 13 24 163 0.82 1 ns
3 Parks_Fields_Hab… 10th … Michi… 13 2 12.5 1 1 ns
4 Parks_Fields_Hab… Fashi… Four … 14 24 181 0.676 1 ns
5 Parks_Fields_Hab… Fashi… Michi… 14 2 14 1 1 ns
6 Parks_Fields_Hab… Four … Michi… 24 2 22 0.874 1 ns kruskal_test(data = recoded_data, formula = Parks_Fields_Beautify ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Parks_Fields_Beautify 54 2.91 3 0.405 Kruskal-Walliswilcox_test(formula = Parks_Fields_Beautify ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Parks_Fields_Bea… 10th … Fashi… 14 14 67 0.12 0.72 ns
2 Parks_Fields_Bea… 10th … Four … 14 24 144. 0.454 1 ns
3 Parks_Fields_Bea… 10th … Michi… 14 2 11 0.673 1 ns
4 Parks_Fields_Bea… Fashi… Four … 14 24 204. 0.223 1 ns
5 Parks_Fields_Bea… Fashi… Michi… 14 2 16 0.777 1 ns
6 Parks_Fields_Bea… Four … Michi… 24 2 21.5 0.834 1 ns kruskal_test(data = recoded_data, formula = Parks_Fields_Prevent_Flooding ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Parks_Fields_Prevent_Flooding 54 8.62 3 0.0347 Kruskal-Walliswilcox_test(formula = Parks_Fields_Prevent_Flooding ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Parks_Fields_Pre… 10th … Fashi… 14 14 42 0.007 0.045 *
2 Parks_Fields_Pre… 10th … Four … 14 24 126. 0.191 0.764 ns
3 Parks_Fields_Pre… 10th … Michi… 14 2 8 0.356 1 ns
4 Parks_Fields_Pre… Fashi… Four … 14 24 232 0.042 0.21 ns
5 Parks_Fields_Pre… Fashi… Michi… 14 2 20 0.336 1 ns
6 Parks_Fields_Pre… Four … Michi… 24 2 21 0.798 1 ns recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Parks_Fields_Water_Supplies, Neighborhood) %>%
rstatix::get_summary_stats(type = "common") %>%
ggplot() +
geom_bar(aes(x = Neighborhood, y = mean, fill = Neighborhood), stat = "identity", alpha = 0.6) +
scale_fill_manual(name = "", values = c("#440154FF", "dodgerblue", "#E57200", "#B8DE29FF"),
labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
labs(x = " ", y = "Mean", title = "Perceptions of Green Space Water Supplies Benefits", subtitle = "",
caption = "Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree") +
theme_minimal() +
theme(legend.position="bottom",
plot.subtitle = element_text(hjust = 0.5),
plot.title = element_text(hjust = 0.5, size = 16),
axis.text.y = element_text(size = 12),
plot.caption = element_text(size = 9),
text = element_text(size = 14)) +
geom_text(aes(label = round(mean, 2), x = Neighborhood, y = mean), vjust = 1.8, color = "white", size = 7)
recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Parks_Fields_Prevent_Flooding, Neighborhood) %>%
rstatix::get_summary_stats(type = "common") %>%
ggplot() +
geom_bar(aes(x = Neighborhood, y = mean, fill = Neighborhood), stat = "identity", alpha = 0.6) +
scale_fill_manual(name = "", values = c("#440154FF", "dodgerblue", "#E57200", "#B8DE29FF"),
labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
labs(x = " ", y = "Mean", title = "Perceptions of Green Space Flood Prevention Benefits", subtitle = "",
caption = "Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree") +
theme_minimal() +
theme(legend.position="bottom",
plot.subtitle = element_text(hjust = 0.5),
plot.title = element_text(hjust = 0.5, size = 16),
axis.text.y = element_text(size = 12),
plot.caption = element_text(size = 9),
text = element_text(size = 14)) +
geom_text(aes(label = round(mean, 2), x = Neighborhood, y = mean), vjust = 1.8, color = "white", size = 7)
There are statistically significant differences (0.05 alpha level) for two questions related to perceived benefits of green space across neighborhoods.
For the Green space such as fields and parks can enhance local water supplies question (kw = 12.0, p = 0.007) respondents from the Fashion Square Mall and Branchlands Area agreed most strongly while respondents from Michie Drive were most neutral. The Wilcox test confirms a significant difference between 10th and Page responses and those from the Fashion Square Mall and Branchlands Area.
For the Green space such as fields and parks can help control stormwater runoff and can help prevent flooding question (kw = 8.62, p = 0.035) respondents from the Fashion Square Mall and Branchlands Area agreed most strongly while respondents from 10th and Page were most neutral. The Wilcox test confirms a significant difference between 10th and Page responses and those from the Fashion Square Mall and Branchlands Area.
kbl(
recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Retention_Ponds_Water_Supplies, Retention_Ponds_Recreation, Retention_Ponds_Habitat,
Retention_Ponds_Beautify, Retention_Ponds_Prevent_Flooding, Neighborhood) %>%
`colnames<-`(c("Retention_Ponds_Water_Supplies", "Retention_Ponds_Recreation", "Retention_Ponds_Habitat",
"Retention_Ponds_Beautify", "Retention_Ponds_Prevent_Flooding", "Neighborhood")) %>%
rstatix::get_summary_stats(show = c("min", "max", "mean", "sd")),
col.names = c("Neighborhood", "Question", "#", "Min", "Max", "Mean", "Std Dev"),
) %>%
kable_paper(bootstrap_options = "striped", full_width = F) | Neighborhood | Question | # | Min | Max | Mean | Std Dev |
|---|---|---|---|---|---|---|
| 10th and Page Neighborhood | Retention_Ponds_Water_Supplies | 14 | 1 | 5 | 3.429 | 1.158 |
| 10th and Page Neighborhood | Retention_Ponds_Recreation | 14 | 1 | 4 | 3.357 | 0.929 |
| 10th and Page Neighborhood | Retention_Ponds_Habitat | 14 | 1 | 5 | 3.571 | 1.222 |
| 10th and Page Neighborhood | Retention_Ponds_Beautify | 14 | 1 | 5 | 3.571 | 1.158 |
| 10th and Page Neighborhood | Retention_Ponds_Prevent_Flooding | 14 | 2 | 5 | 3.643 | 0.929 |
| Fashion Square Mall and Branchlands Area | Retention_Ponds_Water_Supplies | 14 | 4 | 5 | 4.786 | 0.426 |
| Fashion Square Mall and Branchlands Area | Retention_Ponds_Recreation | 14 | 2 | 5 | 3.500 | 0.941 |
| Fashion Square Mall and Branchlands Area | Retention_Ponds_Habitat | 14 | 3 | 5 | 4.500 | 0.650 |
| Fashion Square Mall and Branchlands Area | Retention_Ponds_Beautify | 14 | 3 | 5 | 4.214 | 0.699 |
| Fashion Square Mall and Branchlands Area | Retention_Ponds_Prevent_Flooding | 14 | 4 | 5 | 4.786 | 0.426 |
| Four Seasons Apartments and Townwood Mobile Home Park | Retention_Ponds_Water_Supplies | 24 | 3 | 5 | 3.833 | 0.702 |
| Four Seasons Apartments and Townwood Mobile Home Park | Retention_Ponds_Recreation | 24 | 2 | 5 | 3.708 | 0.690 |
| Four Seasons Apartments and Townwood Mobile Home Park | Retention_Ponds_Habitat | 23 | 3 | 5 | 4.087 | 0.793 |
| Four Seasons Apartments and Townwood Mobile Home Park | Retention_Ponds_Beautify | 24 | 3 | 5 | 4.125 | 0.797 |
| Four Seasons Apartments and Townwood Mobile Home Park | Retention_Ponds_Prevent_Flooding | 24 | 3 | 5 | 4.458 | 0.721 |
| Michie Drive Subsidized Housing | Retention_Ponds_Water_Supplies | 2 | 3 | 5 | 4.000 | 1.414 |
| Michie Drive Subsidized Housing | Retention_Ponds_Recreation | 2 | 2 | 3 | 2.500 | 0.707 |
| Michie Drive Subsidized Housing | Retention_Ponds_Habitat | 2 | 4 | 5 | 4.500 | 0.707 |
| Michie Drive Subsidized Housing | Retention_Ponds_Beautify | 2 | 4 | 4 | 4.000 | 0.000 |
| Michie Drive Subsidized Housing | Retention_Ponds_Prevent_Flooding | 2 | 3 | 5 | 4.000 | 1.414 |
kruskal_test(data = recoded_data, formula = Retention_Ponds_Water_Supplies ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Retention_Ponds_Water_Supplies 54 17.1 3 0.00068 Kruskal-Walliswilcox_test(formula = Retention_Ponds_Water_Supplies ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Retention_Pond… 10th … Fashi… 14 14 26 4.16e-4 0.002 **
2 Retention_Pond… 10th … Four … 14 24 140 3.71e-1 1 ns
3 Retention_Pond… 10th … Michi… 14 2 10.5 6.21e-1 1 ns
4 Retention_Pond… Fashi… Four … 14 24 284 1.76e-4 0.001 **
5 Retention_Pond… Fashi… Michi… 14 2 19.5 2.95e-1 1 ns
6 Retention_Pond… Four … Michi… 24 2 22 8.76e-1 1 ns kruskal_test(data = recoded_data, formula = Retention_Ponds_Recreation ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Retention_Ponds_Recreation 54 4.39 3 0.222 Kruskal-Walliswilcox_test(formula = Retention_Ponds_Recreation ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Retention_Ponds_… 10th … Fashi… 14 14 95 0.902 0.963 ns
2 Retention_Ponds_… 10th … Four … 14 24 138. 0.321 0.963 ns
3 Retention_Ponds_… 10th … Michi… 14 2 22.5 0.167 0.835 ns
4 Retention_Ponds_… Fashi… Four … 14 24 144. 0.448 0.963 ns
5 Retention_Ponds_… Fashi… Michi… 14 2 22.5 0.183 0.835 ns
6 Retention_Ponds_… Four … Michi… 24 2 43 0.049 0.292 ns kruskal_test(data = recoded_data, formula = Retention_Ponds_Habitat ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Retention_Ponds_Habitat 54 6.00 3 0.111 Kruskal-Walliswilcox_test(formula = Retention_Ponds_Habitat ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Retention_Ponds_… 10th … Fashi… 14 14 53 0.03 0.181 ns
2 Retention_Ponds_… 10th … Four … 14 23 122. 0.211 0.844 ns
3 Retention_Ponds_… 10th … Michi… 14 2 7.5 0.322 0.966 ns
4 Retention_Ponds_… Fashi… Four … 14 23 208. 0.12 0.6 ns
5 Retention_Ponds_… Fashi… Michi… 14 2 14.5 1 1 ns
6 Retention_Ponds_… Four … Michi… 23 2 16.5 0.521 1 ns kruskal_test(data = recoded_data, formula = Retention_Ponds_Beautify ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Retention_Ponds_Beautify 54 3.04 3 0.385 Kruskal-Walliswilcox_test(formula = Retention_Ponds_Beautify ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Retention_Ponds_… 10th … Fashi… 14 14 66 0.125 0.75 ns
2 Retention_Ponds_… 10th … Four … 14 24 123 0.156 0.78 ns
3 Retention_Ponds_… 10th … Michi… 14 2 11 0.675 1 ns
4 Retention_Ponds_… Fashi… Four … 14 24 177 0.782 1 ns
5 Retention_Ponds_… Fashi… Michi… 14 2 17 0.656 1 ns
6 Retention_Ponds_… Four … Michi… 24 2 27 0.797 1 ns kruskal_test(data = recoded_data, formula = Retention_Ponds_Prevent_Flooding ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Retention_Ponds_Prevent_Flooding 54 13.9 3 0.00308 Kruskal-Walliswilcox_test(formula = Retention_Ponds_Prevent_Flooding ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Retention_Ponds… 10th … Fashi… 14 14 27.5 0.0005 0.003 **
2 Retention_Ponds… 10th … Four … 14 24 84 0.007 0.034 *
3 Retention_Ponds… 10th … Michi… 14 2 11.5 0.737 1 ns
4 Retention_Ponds… Fashi… Four … 14 24 206. 0.168 0.672 ns
5 Retention_Ponds… Fashi… Michi… 14 2 19.5 0.295 0.885 ns
6 Retention_Ponds… Four … Michi… 24 2 29.5 0.587 1 ns recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Retention_Ponds_Water_Supplies, Neighborhood) %>%
rstatix::get_summary_stats(type = "common") %>%
ggplot() +
geom_bar(aes(x = Neighborhood, y = mean, fill = Neighborhood), stat = "identity", alpha = 0.6) +
scale_fill_manual(name = "", values = c("#440154FF", "dodgerblue", "#E57200", "#B8DE29FF"),
labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
labs(x = " ", y = "Mean", title = "Perceptions of Stormwater Retention Pond Water Supply Benefits", subtitle = "",
caption = "Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree") +
theme_minimal() +
theme(legend.position="bottom",
plot.subtitle = element_text(hjust = 0.5),
plot.title = element_text(hjust = 0.5, size = 16),
axis.text.y = element_text(size = 12),
plot.caption = element_text(size = 9),
text = element_text(size = 14)) +
geom_text(aes(label = round(mean, 2), x = Neighborhood, y = mean), vjust = 1.8, color = "white", size = 7)
recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Retention_Ponds_Prevent_Flooding, Neighborhood) %>%
rstatix::get_summary_stats(type = "common") %>%
ggplot() +
geom_bar(aes(x = Neighborhood, y = mean, fill = Neighborhood), stat = "identity", alpha = 0.6) +
scale_fill_manual(name = "", values = c("#440154FF", "dodgerblue", "#E57200", "#B8DE29FF"),
labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
labs(x = " ", y = "Mean", title = "Perceptions of Stormwater Retention Pond Flood Prevention Benefits", subtitle = "",
caption = "Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree") +
theme_minimal() +
theme(legend.position="bottom",
plot.subtitle = element_text(hjust = 0.5),
plot.title = element_text(hjust = 0.5, size = 16),
axis.text.y = element_text(size = 12),
plot.caption = element_text(size = 9),
text = element_text(size = 14)) +
geom_text(aes(label = round(mean, 2), x = Neighborhood, y = mean), vjust = 1.8, color = "white", size = 7)
There are statistically significant differences (0.05 alpha level) for two questions related to perceived benefits of stormwater retention ponds across neighborhoods.
For the Stormwater retention ponds can enhance local water supplies. question (kw = 17.1, p = 0.001) respondents from the Fashion Square Mall and Branchlands Area agreed most strongly while respondents from 10th and Page were most neutral. The Wilcox test confirms a significant difference between 10th and Page responses and those from the Fashion Square Mall and Branchlands Area as well as between the Fashion Square Mall and Branchlands Area and the Four Seasons Apartments and Townwood Mobile Home Park.
For the Stormwater retention ponds can help control stormwater runoff and can help prevent flooding question (kw = 13.9, p = 0.003) respondents from the Fashion Square Mall and Branchlands Area agreed most strongly while respondents from 10th and Page were most neutral. The Wilcox test confirms a significant difference between 10th and Page responses and those from both the Four Seasons Apartments and Townwood Mobile Home Park and the Fashion Square Mall and Branchlands Area.
kbl(
recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Tree_Planting_Water_Supplies, Tree_Planting_Recreation, Tree_Planting_Habitat,
Tree_Planting_Beautify, Tree_Planting_Prevent_Flooding, Neighborhood) %>%
`colnames<-`(c("Tree_Planting_Water_Supplies", "Tree_Planting_Recreation", "Tree_Planting_Habitat",
"Tree_Planting_Beautify", "Tree_Planting_Prevent_Flooding", "Neighborhood")) %>%
rstatix::get_summary_stats(show = c("min", "max", "mean", "sd")),
col.names = c("Neighborhood", "Question", "#", "Min", "Max", "Mean", "Std Dev"),
) %>%
kable_paper(bootstrap_options = "striped", full_width = F) | Neighborhood | Question | # | Min | Max | Mean | Std Dev |
|---|---|---|---|---|---|---|
| 10th and Page Neighborhood | Tree_Planting_Water_Supplies | 14 | 2 | 5 | 3.429 | 1.016 |
| 10th and Page Neighborhood | Tree_Planting_Recreation | 14 | 2 | 5 | 4.000 | 0.877 |
| 10th and Page Neighborhood | Tree_Planting_Habitat | 14 | 3 | 5 | 4.286 | 0.825 |
| 10th and Page Neighborhood | Tree_Planting_Beautify | 14 | 3 | 5 | 4.071 | 0.829 |
| 10th and Page Neighborhood | Tree_Planting_Prevent_Flooding | 14 | 2 | 5 | 3.714 | 0.914 |
| Fashion Square Mall and Branchlands Area | Tree_Planting_Water_Supplies | 14 | 3 | 5 | 4.500 | 0.650 |
| Fashion Square Mall and Branchlands Area | Tree_Planting_Recreation | 14 | 2 | 5 | 4.429 | 0.938 |
| Fashion Square Mall and Branchlands Area | Tree_Planting_Habitat | 14 | 3 | 5 | 4.714 | 0.611 |
| Fashion Square Mall and Branchlands Area | Tree_Planting_Beautify | 13 | 4 | 5 | 4.615 | 0.506 |
| Fashion Square Mall and Branchlands Area | Tree_Planting_Prevent_Flooding | 14 | 3 | 5 | 4.429 | 0.756 |
| Four Seasons Apartments and Townwood Mobile Home Park | Tree_Planting_Water_Supplies | 24 | 2 | 5 | 4.125 | 0.850 |
| Four Seasons Apartments and Townwood Mobile Home Park | Tree_Planting_Recreation | 24 | 3 | 5 | 4.000 | 0.722 |
| Four Seasons Apartments and Townwood Mobile Home Park | Tree_Planting_Habitat | 24 | 3 | 5 | 4.583 | 0.654 |
| Four Seasons Apartments and Townwood Mobile Home Park | Tree_Planting_Beautify | 24 | 3 | 5 | 4.583 | 0.654 |
| Four Seasons Apartments and Townwood Mobile Home Park | Tree_Planting_Prevent_Flooding | 24 | 3 | 5 | 4.333 | 0.761 |
| Michie Drive Subsidized Housing | Tree_Planting_Water_Supplies | 2 | 3 | 3 | 3.000 | 0.000 |
| Michie Drive Subsidized Housing | Tree_Planting_Recreation | 2 | 3 | 3 | 3.000 | 0.000 |
| Michie Drive Subsidized Housing | Tree_Planting_Habitat | 2 | 4 | 5 | 4.500 | 0.707 |
| Michie Drive Subsidized Housing | Tree_Planting_Beautify | 2 | 4 | 5 | 4.500 | 0.707 |
| Michie Drive Subsidized Housing | Tree_Planting_Prevent_Flooding | 2 | 3 | 5 | 4.000 | 1.414 |
kruskal_test(data = recoded_data, formula = Tree_Planting_Water_Supplies ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Tree_Planting_Water_Supplies 54 11.8 3 0.00809 Kruskal-Walliswilcox_test(formula = Tree_Planting_Water_Supplies ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Tree_Planting_Wa… 10th … Fashi… 14 14 39.5 0.005 0.03 *
2 Tree_Planting_Wa… 10th … Four … 14 24 102. 0.039 0.154 ns
3 Tree_Planting_Wa… 10th … Michi… 14 2 18 0.561 0.561 ns
4 Tree_Planting_Wa… Fashi… Four … 14 24 209 0.183 0.366 ns
5 Tree_Planting_Wa… Fashi… Michi… 14 2 27 0.03 0.152 ns
6 Tree_Planting_Wa… Four … Michi… 24 2 42 0.074 0.223 ns kruskal_test(data = recoded_data, formula = Tree_Planting_Recreation ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Tree_Planting_Recreation 54 7.49 3 0.0578 Kruskal-Walliswilcox_test(formula = Tree_Planting_Recreation ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Tree_Planting_Re… 10th … Fashi… 14 14 66 0.117 0.327 ns
2 Tree_Planting_Re… 10th … Four … 14 24 174 0.857 0.857 ns
3 Tree_Planting_Re… 10th … Michi… 14 2 24 0.109 0.327 ns
4 Tree_Planting_Re… Fashi… Four … 14 24 228 0.054 0.322 ns
5 Tree_Planting_Re… Fashi… Michi… 14 2 25 0.064 0.322 ns
6 Tree_Planting_Re… Four … Michi… 24 2 42 0.07 0.322 ns kruskal_test(data = recoded_data, formula = Tree_Planting_Habitat ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Tree_Planting_Habitat 54 2.85 3 0.416 Kruskal-Walliswilcox_test(formula = Tree_Planting_Habitat ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Tree_Planting_Ha… 10th … Fashi… 14 14 69 0.123 0.738 ns
2 Tree_Planting_Ha… 10th … Four … 14 24 135 0.259 1 ns
3 Tree_Planting_Ha… 10th … Michi… 14 2 12.5 0.863 1 ns
4 Tree_Planting_Ha… Fashi… Four … 14 24 187 0.481 1 ns
5 Tree_Planting_Ha… Fashi… Michi… 14 2 17.5 0.529 1 ns
6 Tree_Planting_Ha… Four … Michi… 24 2 27 0.774 1 ns kruskal_test(data = recoded_data, formula = Tree_Planting_Beautify ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Tree_Planting_Beautify 54 4.92 3 0.178 Kruskal-Walliswilcox_test(formula = Tree_Planting_Beautify ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Tree_Planting_Be… 10th … Fashi… 14 13 57.5 0.08 0.399 ns
2 Tree_Planting_Be… 10th … Four … 14 24 109 0.048 0.29 ns
3 Tree_Planting_Be… 10th … Michi… 14 2 10 0.554 1 ns
4 Tree_Planting_Be… Fashi… Four … 13 24 153 0.924 1 ns
5 Tree_Planting_Be… Fashi… Michi… 13 2 14.5 0.842 1 ns
6 Tree_Planting_Be… Four … Michi… 24 2 27 0.774 1 ns kruskal_test(data = recoded_data, formula = Tree_Planting_Prevent_Flooding ~ Neighborhood)# A tibble: 1 × 6
.y. n statistic df p method
* <chr> <int> <dbl> <int> <dbl> <chr>
1 Tree_Planting_Prevent_Flooding 54 5.72 3 0.126 Kruskal-Walliswilcox_test(formula = Tree_Planting_Prevent_Flooding ~ Neighborhood, data = recoded_data)# A tibble: 6 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 Tree_Planting_Pr… 10th … Fashi… 14 14 55 0.039 0.232 ns
2 Tree_Planting_Pr… 10th … Four … 14 24 104 0.041 0.232 ns
3 Tree_Planting_Pr… 10th … Michi… 14 2 12 0.802 1 ns
4 Tree_Planting_Pr… Fashi… Four … 14 24 180 0.701 1 ns
5 Tree_Planting_Pr… Fashi… Michi… 14 2 17 0.658 1 ns
6 Tree_Planting_Pr… Four … Michi… 24 2 28 0.713 1 ns recoded_data %>%
group_by(Neighborhood) %>%
dplyr::select(Tree_Planting_Water_Supplies, Neighborhood) %>%
rstatix::get_summary_stats(type = "common") %>%
ggplot() +
geom_bar(aes(x = Neighborhood, y = mean, fill = Neighborhood), stat = "identity", alpha = 0.6) +
scale_fill_manual(name = "", values = c("#440154FF", "dodgerblue", "#E57200", "#B8DE29FF"),
labels = c("10th and Page \n Neighborhood", "Fashion Square Mall \n and Branchlands Area",
"Four Seasons Apartments \n and Townwood Mobile \n Home Park",
"Michie Drive \n Subsidized Housing")) +
labs(x = " ", y = "Mean", title = "Perceptions of Tree Planting Water Supply Benefits", subtitle = "",
caption = "Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree") +
theme_minimal() +
theme(legend.position="bottom",
plot.subtitle = element_text(hjust = 0.5),
plot.title = element_text(hjust = 0.5, size = 16),
axis.text.y = element_text(size = 12),
plot.caption = element_text(size = 9),
text = element_text(size = 14)) +
geom_text(aes(label = round(mean, 2), x = Neighborhood, y = mean), vjust = 1.8, color = "white", size = 7)
There are statistically significant differences (0.05 alpha level) for two questions related to perceived benefits of tree planting across neighborhoods.
For the Planting more trees can enhance local water supplies question (kw = 11.8, p = 0.008) respondents from the Fashion Square Mall and Branchlands Area agreed most strongly while respondents from Michie Drive and 10th and Page were most neutral. The Wilcox test confirms a significant difference between 10th and Page responses and those from the Fashion Square Mall and Branchlands Area.
Two questions explore respondents preferences for NbS interventions where they live:
Which type of NbS element would you prefer to see more of in your community?
List any location(s) you think should be prioritized for NbS investments in future?
The responses to these questions are presented below.
prefs_in_order <- c("Green Roofs", "Green Space", "Rain Gardens", "Stormwater Retention Ponds", "Trees")
recoded_data %>%
tabyl(I_Want_More) %>%
`colnames<-`(c("NbS", "Number", "Percent")) %>%
ggplot() +
geom_bar(aes(y = Number, x = factor(NbS, prefs_in_order)), stat="identity", fill = "green4", alpha = 0.85) +
scale_x_discrete(labels = c("Green Roofs", "Green Space", "Rain Gardens", "Stormwater Retention Ponds", "Trees"),
name = "NbS Intervention") +
labs(title = "Frequency of Preferred NbS Intervention") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
axis.text.x = element_text(size = 12),
text = element_text(size = 18)) +
geom_text(aes(label = Number, x = NbS, y = Number), vjust = 1.8, color = "white", size = 7)
# List any location(s) you think should be prioritized for NbS investments
to_drop <- c("N/a", "N/A", "n/a")
nbs_priority_locations <- data_1 %>%
filter(is.na(`List any location(s) you think should be prioritized for NbS investments in future?`) == FALSE) %>%
dplyr::select(`List any location(s) you think should be prioritized for NbS investments in future?`, Neighborhood) %>%
filter(! `List any location(s) you think should be prioritized for NbS investments in future?` %in% to_drop)
kbl(
nbs_priority_locations %>%
arrange(Neighborhood)
) %>%
kable_paper(bootstrap_options = "striped", full_width = F)| List any location(s) you think should be prioritized for NbS investments in future? | Neighborhood |
|---|---|
| New Orleans | 10th and Page Neighborhood |
| Prospect Ave | 10th and Page Neighborhood |
| 10th & Page neighborhood | 10th and Page Neighborhood |
| Places on UVA grounds around newcomb. The UVA corner always has significant flooding | 10th and Page Neighborhood |
| Branchlands development, where I live, could benefit greatly from a porous pavement. Instead, they "sealcoat" it with a toxic tar far too frequently | Fashion Square Mall and Branchlands Area |
| area around the fashion square mall (where I live) | Fashion Square Mall and Branchlands Area |
| Charlottesville Inner city and new developments | Fashion Square Mall and Branchlands Area |
| If my neighborhood HOA got a grant to put in a rain garden I think a lot of my neighbors would like it. | Fashion Square Mall and Branchlands Area |
| Greenbriar | Fashion Square Mall and Branchlands Area |
| Medical school, North Charlottesville | Fashion Square Mall and Branchlands Area |
| city of Charlottesville | Four Seasons Apartments and Townwood Mobile Home Park |
| Belmont | Four Seasons Apartments and Townwood Mobile Home Park |
| Pond on Hillsdale and Greenbrier and along Meadowcreek Trail via Rivanna Trail Foundation | Four Seasons Apartments and Townwood Mobile Home Park |
| Stonefield Commons and the surrounding highway area | Four Seasons Apartments and Townwood Mobile Home Park |
| Commonwealth/ Four Seasons | Four Seasons Apartments and Townwood Mobile Home Park |
| The commonwealth neighborhood in Charlottesville | Four Seasons Apartments and Townwood Mobile Home Park |
| Many locations in the Rivanna watershed | Four Seasons Apartments and Townwood Mobile Home Park |
| Charlottesville | Four Seasons Apartments and Townwood Mobile Home Park |
| Four Seasons | Four Seasons Apartments and Townwood Mobile Home Park |
| low income communities | Four Seasons Apartments and Townwood Mobile Home Park |
| White House | Four Seasons Apartments and Townwood Mobile Home Park |
| Common area used as play space for kids | Four Seasons Apartments and Townwood Mobile Home Park |
| Four Seasons | Four Seasons Apartments and Townwood Mobile Home Park |
| Charlottesville along the highway 29 corridor north of town is largely blighted. Near my development there are two large parking lots for shopping centers that are mostly or entirely abandoned buildings. No greenery, impermeable surfaces everywhere. | Michie Drive Subsidized Housing |
The most common desired NbS intervention was rain gardens, followed by stormwater retention ponds and tree planting. The open-ended responses to the question of where those interventions should be made range from very specific (e.g., “Pond on Hillsdale and Greenbrier and along Meadowcreek Trail via Rivanna Trail Foundation” to very vague (e.g., “New Orleans” and “city of Charlottesville”).
Next steps include exploring the polygons drawn by respondents to this survey that show areas that they believe:
On a final note—the response rate from the Michie Drive study area has been very low (3/33) at 9.1%. We have email addressed for 10 of these individuals I will write to them directly this week to encourage them to participate.